The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects

Blankertz, Benjamin; Dornhege, Guido; Krauledat, Matthias; Müller, Klaus Robert; Curio, Gabriel

doi:10.1016/j.neuroimage.2007.01.051

Cited by 792 publications

(440 citation statements)

References 38 publications

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“…Thus, we may cautiously conclude that the funnel feedback may support the initial training phase and represents an alternative feedback for an SMR BCI. Another explanation for the significantly better performance during the initial training session could be due to the fact that we did not include any online adaptation (Blankertz et al, 2007). Classification accuracy is certainly affected by inter-session non-stationarity of brain patterns and the uncertainty metric used for the funnel might be even more affected by this issue.…”

Section: Discussionmentioning

confidence: 99%

“…Approaches to alleviate this phenomenon have been explored, such as improved signal processing . Blankertz et al (2007) demonstrated that participants, who had no peak of the sensory motor idle rhythm at the beginning of the experiment, could develop such peak during the course of the session with an end-user-optimized state-of-the-art classifier. They developed the BBCI -a machine learning BCI approach -which provides BCI control during the first session after 20 min screening period.…”

Section: Discussionmentioning

confidence: 99%

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The effect of multimodal and enriched feedback on SMR-BCI performance

Sollfrank

Ramsay

Perdikis

et al. 2016

Clinical Neurophysiology

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The effect of multimodal and enriched feedback on SMR-BCI performance

Sollfrank

Ramsay

Perdikis

et al. 2016

Clinical Neurophysiology

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“…The spatial filter maximizes the variance of signals of one class and at the same time minimizes the variance of signals of the other class. Because band power is equal to the variance of band-pass filtered signals, CSP performs very well as a spatial filter for detecting ERS/ERD in EEG measurements and has been well used in in BCI systems [20,21,22]. In this study, we extract one feature every 0.2 second, so a sequence of features Y (i) is obtained in a single trail (7 seconds).…”

Section: Common Spatial Patternsmentioning

confidence: 99%

Removing Unrelated Features Based on Linear Dynamical System for Motor-Imagery-Based Brain-Computer Interface

Shi

2011

Neural Information Processing

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Abstract. Common spatial pattern (CSP) is very successful in constructing spatial filters for detecting event-related synchronization and event-related desynchronization. In statistics, a CSP filter can optimally separate the motor-imagery-related components. However, for a single trail, the EEG features extracted after a CSP filter still include features not related to motor imagery. In this study, we introduce a linear dynamical system (LDS) approach to motor-imagery-based brain-computer interface (MI-BCI) to reduce the influence of these unrelated EEG features. This study is conducted on a BCI competition data set, which comprises EEG signals from several subjects performing various movements. Experimental results show that our proposed algorithm with LDS performs better than a traditional algorithm on average. The results reveal a promising direction in the application of LDS-based approach to MI-BCI.

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“…The dataset used is Dataset 1 from BCI Competition IV [23] which contain recorded EEG data for motor imagery task perform by four healthy human subjects (a, b, f and g). In the experiment, each subject was asked to select two mental tasks to perform out of three tasks which are left hand, right hand or foot movements.…”

Section: Data Descriptionmentioning

confidence: 99%

The Performance Analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification Based on EEG Signal

et al. 2017

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Abstract. Most EEG-based motor imagery classification research focuses on the feature extraction phase of machine learning, neglecting the crucial part for accurate classification which is the classification. In contrast, this paper concentrates on the classifier development where it thoroughly studies the performance analysis of k-Nearest Neighbour (k-NN) classifier on EEG data. In the literature, the Euclidean distance metric is routinely applied for EEG data classification. However, no thorough study has been conducted to evaluate the effect of other distance metrics to the classification accuracy. Therefore, this paper studies the effectiveness of five distance metrics of k-NN: Manhattan, Euclidean, Minkowski, Chebychev and Hamming. The experiment shows that the distance computations that provides the highest classification accuracy is the Minkowski distance with 70.08%. Hence, this demonstrates the significant effect of distance metrics to the k-NN accuracy where the Minknowski distance gives higher accuracy compared to the Euclidean. Our result also shows that the accuracy of k-NN is comparable to Support Vector Machine (SVM) with lower complexity for EEG classification.

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The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects

Cited by 792 publications

References 38 publications

The effect of multimodal and enriched feedback on SMR-BCI performance

The effect of multimodal and enriched feedback on SMR-BCI performance

Removing Unrelated Features Based on Linear Dynamical System for Motor-Imagery-Based Brain-Computer Interface

The Performance Analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification Based on EEG Signal

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